| Abstract|| |
Context: Assessment forms an important part of the early intervention in autism spectrum disorders (ASD). However, there is not much choice with regard to tools and methods for early identification and assessment of ASD.
Aims: The aim is to develop a scale for identifying ASD during infancy and early childhood by mapping empirically supported skill behaviors and excess behaviors.
Settings and Design: The study was conducted in community-based organisations and the local communities. Moreover, it employed a post facto research design, with survey method.
Materials and Methods: Following the standard procedures to pool items and standardization, a scale was developed and administered to three groups of children (N = 190) including children with autism (n = 100), global developmental delay suggestive of mental retardation (n = 40) and neurotypicals (n = 50) in the age range of 1.5–6 years. The ICD-10 Classification of Mental and Behaviooural Disorders and Baby and Infant Screen for Children with aUtIsm Traits (BISCUIT) were the gold standards to diagnose ASD.
Statistical Analysis Used: Descriptive statistics (frequency, percentages, median, and quartile deviation), Pearson's correlation, Cronbach's α, factor analysis, and binary logistic regression analysis with receiver operating characteristic curve were performed.
Results: The new scale also demonstrated a high diagnostic efficiency by yielding a hit rate of 0.89, specificity of 0.90, and sensitivity of 0.88.
Conclusions: The new scale can be used for early identification of ASD in the Indian population, though further validation with large population is required.
Keywords: Autism spectrum disorder, Baby and Infant Screen for Children with aUtIsm, early childhood, identification, India
|How to cite this article:|
Kishore M T, Menon DK, Binukumar B. Development of a scale for identifying autism spectrum disorder during early childhood. Indian J Psychiatry 2018;60:438-44
| Introduction|| |
Autism is a neurodevelopmental disorder that affects sociocommunication skills and is associated with restricted interests and repetitive behaviors. A few children seem to develop autism after some period of normal development, but current understanding is that autism is generally present from birth,, and intense therapy given in the first 3 years of age helps improve the condition significantly., Somewhat paradoxically, however, our knowledge of autism as it is expressed in infancy is quite limited. Since there are no biological markers for autism,, a diagnosis of autism spectrum disorders (ASD) need to be made on the basis of a behavioral profile, which is characterized by both the absence of typical behaviors as well as the presence of atypical behaviors. This scenario may explain why autism is usually not diagnosed until the age of 3 years. Hence, there is a scope to understand the condition from a developmental and behavioral perspective as far as autism in infancy is concerned.[8.9]
Existing diagnostic systems, both the Diagnostic and Statistical Manual of Mental Disorders 5th Revision and the International Classification of Diseases. emphasize that a child must have shown abnormalities in social interaction, language as used in social communication or symbolic/imaginative play before the age of 3 years to receive a diagnosis of autism. However, many behavioral criteria are rare (for example, stereotypes) or are not appropriate (for example, language communication deficits) under the age of 2 years. There could be several, nonspecific developmental or behavioral problems during early age. Therefore, there is a scope for close monitoring and surveillance of children who show atypical behaviors and developmental problems during early stages, though such atypicalities are not to the level of a syndrome. There is also a need for scales that capture the atypical behaviors and developmental concerns during infancy and early childhood that may identify autism from other neurodevelopmental disorders.
At present, there are at least 12 scales for early identification of ASD. They are: pervasive developmental disorders screening test-II, screening tool for Autism in 2-year-old, 1st year Inventory, Communication and Symbolic Behavior Scales Developmental Profile, revised psychoeducational profile, parent concern checklist, early screening of Autistic traits questionnaire, young autism and other developmental disorders checkup tool, Preschool Imitation and Praxis Scale, social communication questionnaire, checklist for early signs of developmental disorders, Autistic behavioral indicators instrument, and baby and infant screen for children with aUtIsm traits. However, a comprehensive review indicates that none of the existing tools are suitable for the identification of autism during infancy.,,
Universal screening procedures appear to be impeded by the varying patterns of onset and manifestation of ASD. More generally, well-documented diagnostic instruments may work well after age 3–4 years or past a certain developmental level, but their use is not clearly established for infancy. Only trained clinicians can detect autistic features in children under the age of two, but they do not often get to see such young children., However, large number of children and infants are also seen by other professionals such as special educators, psychologists, and community health workers, who may need appropriate screening tools to identify children at-risk for autism. Hence, there is a scope for the development of a scale for identifying autism during infancy and early childhood, particularly in the Indian context. The objective of the present study was to the development of a tool to identify autism based on the early developmental and behavioral pattern in children later diagnosed with autism.
| Materials and Methods|| |
This is a post facto research design, with survey method. That is, this study surveyed the autism features in children diagnosed with autism.
Baby and infant screen for children with aUtIsm traits
This is a standardized scale designed to be completed by parent/caretakers of infants and toddlers ages 17–37 months. The scale comprises four components. The purpose of this tool is to both aid in the diagnosis of ASD and provide a means for treatment monitoring in the toddler years. The first component is an informant-based measure designed to assess symptoms of Autistic disorder and pervasive developmental disorder not otherwise specified in infants and toddlers. It consists of 62 items that are read to a parent/caretaker. The parent/caretaker is instructed to rate each item by comparing the child to other children his/her age with the following ratings: 0 = not different; no impairment, 1 = somewhat different; mild impairment, and 2 = very different; severe impairment. The second and third component assess symptoms of emotional difficulties and challenging behaviors that commonly occur with ASD, respectively. The fourth component provides supplemental information related to child's response to name, interest in peers or others, eye contact, pretend play, and engagement in reciprocal play. However, information on the fourth component is purely based on the information obtained from the first three Parts. In this study, all parts except Baby and Infant Screen for Children with aUtIsm Traits (BISCUIT) Part 3 were used. A score of 21 or above in Part 1 considered to indicate at-risk for ASD.
The general development was assessed using the developmental screening test and the adaptive behavior by the Vineland Social Maturity Scale. These scales are extensively used in India for developmental assessment and are known to complement intellectual assessment well.
The sample consisted of 190 children who were <6 years, and had an the ICD-10 Classification of Mental and Behaviooural Disorders primary diagnosis of autism (n = 100), global developmental delay suggestive of mental retardation (n = 40); and generally developing children (n = 50). Gender of the child and socioeconomic status of the family was no bar. The clinical groups (autism and mental retardation) was recruited from six organizations across the country, who have been working in the field of developmental disabilities, and gave consent to participate in the study.
After obtaining the ethical approval from the Board of Ethics of the University of Hyderabad, where the work was planned and initiated, the study was carried out in several phases as follows:
A comprehensive review by Matson et al. indicates that we would be biased in the usual screening of autism when we focused on features specific to autism. The available research shows a marked overlap between the core symptoms of autism, challenging behaviors, and some specific types of psychopathology. The extreme clinical heterogeneity of autism requires us to define symptoms that are not necessarily included in the standard definitions of autism. Hence, best practice for early identification and diagnosis of autism is that instruments should go well beyond measuring core symptoms of autism. Keeping this in view items were pooled from several sources, with an understanding that children with ASD can have a wide range of challenging behaviors and skill deficits.,,,,,,,,,,,,, However, the following four sources are credited for the item pool as they have included all major, important studies on the early manifestation of ASD: Volkmar et al.,, Guinchat et al., and Zwaigenbaum.
The pooled items were worded in objective terms. Ambiguous descriptions were avoided. When symptoms appeared across several domains, they were retained only in one domain. Since this study did not focus on domain-specific item categorization, further procedures were not planned to see whether the particular item fits into specific domain or not.
Establishing validity and reliability
Face validity and content validity were obtained by giving the item pool to a panel of 15 experts from diverse fields such as clinical psychology (n = 7), psychiatry (n = 2), speech pathology (n = 2), pediatrics (n = 1), and special education (n = 3), who have extensive experience in working with children with ASD. According to their suggestions, few items were reworded. However, the panelists agreed that all the items were relevant and the item-pool was comprehensive. Therefore, all the items pooled were retained for field trial. Interrater reliability was established with a mixed group of 20 children aged 6–36 months. The group included 10 children with developmental delay, six children with hearing, speech, and communication deficits and four children with ASD. Data for interrater reliability was collected from a community-based, nongovernment organization working for children with developmental disabilities in Kurnool, Andhra Pradesh for the center was willing to participate in the study. The interrater reliability for the overall scale was satisfactory (r = 0.79; P < 0.01).
This item pool along with BISCUIT was translated to Bengali, Hindi, Tamil, and Telugu by using standard guidelines followed in biomedical research.
Administering the scales on the target population
During this phase, 100 children with autism, 40 children with global developmental delay and 50 children with typical development were included. This sample also includes those who participated in the pilot study on the interrater reliability of the scale. Clinical diagnoses were based on ICD-10 criteria. Qualified special educators, speech therapists, psychologists participated in data collection under the supervision of the experts in the respective organization that has agreed to participate in the study. Initially, the purpose of the study was explained to the prospective participants by giving a flyer and further clarifying the doubts, if any. Once the informed consent was obtained, the scale was administered individually whereby information was collected by interviewing the mothers and by directly observing the children as feasible. Whenever the mothers were not sure of a particular behavior in the child, the same was cross-checked with their respective spouses and other family members involved in caregiving of the index child.
Data were analyzed using the Stata software (version 12 SE; StataCorp, TX, USA). Descriptive statistics such as frequency (percentages), and median quartile deviation were used to summarize the qualitative and quantitative variables, respectively. Adopting Steiner's guidelines, Cronbach's α was used for checking the internal consistency and set a criterion that α values within the range of 0.50–0.90 were considered to denote good internal consistency. Factor analysis was performed for data reduction using the concept of tetrachoric correlation with iterated principal factors followed by oblique rotation to analyze the correlation matrix and estimates the communalities iteratively because the Pearson's correlation matrix conveys misleading relationship for dichotomous data. Further binary logistic regression analysis followed by receiver operating characteristic (ROC) curve was performed to specify the classification criteria for autism.
| Results|| |
Descriptive analysis indicates that there were 53 males (60%) and 37 females (40%) in the no-autism group; and 78 males (78%) and 22 females (22%) in the autism group in the age group of 1.5 years to 6 years. Majority in both groups belonged to middle socioeconomic status (85%), followed by low-economic status (10%) and high-economic status (5%). The groups differed regarding fathers' occupation though majority in both cases working in the private sector. There were no significant group differences with reference to mothers' occupation, were the majority were housewives; or domicile, family history of disability and chronic illnesses, birth order, number of siblings, family type, and sociooccupational status. The mean age of the children with autism was 51.24 (standard deviation (SD) 14.56) months and those with no autism was 36.99 (SD 19.52) months, and the mean difference observed as statistically significant (t = 5.74; df = 188; P < 0.01). The median DQ for no-autism group was 90 (interquartile deviation = 32) as compared to 20 (interquartile deviation = 24) for the autism group (z = −7.75; P < 0.01). The median SQ for no-autism group was 90 (interquartile deviation = 30) as compared to 25 (interquartile deviation = 25) for the autism group (z = −7.02; P < 0.01).
[Table 1] presents the items with high internal consistency. Adopting Steiner's criteria, Forty items were identified as having high internal consistency (i.e., α > 0.50). Redundancy was ruled out as none of the items had α value of >0.90. [Table 2] presents the factor loading obtained by factor analysis using tetrachoric correlation through iterated principal factor method. The loading provided in [Table 2] represent the correlations obtained between common factors and variables of interest. Only those variables with correlation above 0.30 have been reported. The uniqueness of the items, that is, the percentage of variance for the variable that is unexplained by the common factors, has been reported. “1− uniqueness” is communality. The greater the uniqueness is, the more likely that it is not just a measurement error. In this case, all the variables have lower uniqueness and higher communality, which means that the variable is well explained by the factors. The forty items have had loading on three factors, with factor 1 having the highest load, followed by factors 2 and 3. It was noted that all the items loaded on factor 2 and 3 were not exclusive to them but had loaded on factor 1 as well. The items in factor 1 indicate sociocommunication skills, Factor 2 sensory-motor issues and Factor 3 social-adaptive skills. The Eigenvalues and proportion of variance explained by the three factors are provided in [Table 2]. It was observed that factor 1 (sociocommunication skills) accounted for 86% of the variance; whereas factor 2 (sensory-motor issues) and factor 3 (social-adaptive skills) 8% and 6% of the variance, respectively. The Screeplot indicates that factor 1 has the largest Eigen value therefore has the most variance in the total scores of the new scale [Figure 1]. [Table 3] presents the correlation matrix of the factors by using the iterated principal factors method with oblique rotation. Moreover, the correlation matrix shows a high correlation between factor 1 and factor 2. It retained three factors with 114 params per rotation. The results indicate that the three factors were positively correlated. [Table 4] shows diagnostic efficiency statistics of the new scale with different cutoffs across different age groups based on the ROC plotted with the predicted probability through binary logistic regression. The table clearly indicates that cutoff 5 (i. e., 4.5 rounded off to the nearest whole number) could be used as a cutoff for age groups below 48 months to identify autism. For those above 48 months, cutoff 9 (i.e., 8.5 rounded off to the nearest whole number) could be used as a cutoff on this scale. [Table 5] shows that both the new scale and BISCUIT have same hit rate but the new scale has a slightly lower sensitivity (0.88) but better specificity (0.90), positive predictive validity (0.90), and likelihood ratio (8.80) than BISCUIT.
|Table 4: Diagnostic efficiency statistics for different cut-offs across various age groups|
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| Discussion|| |
Early indicators of autism could comprise behaviors and developmental manifestations that are not specific to autism. Therefore, we need to focus on both specific and non-specific behavioral and developmental indicators to identify autism in early childhood. The scale developed in this study has 40 items with high internal consistency and relatively a high loading on the sociocommunication factor. A high loading and variance of sociocommunication deficits reiterate that they are cardinal feature of autism. The items that loaded on sensory issues and social-adaptive skills also loaded on sociocommunication skills. This may suggest that the adaptive behaviors and sensory issues may be evident through sociocommunication deficits. These findings are in line with the existing literature that sociocommunication deficits along with social adaptation and sensory issues are common in ASD.,, Second, they also suggest an extreme clinical heterogeneity of ASD and many symptoms are not necessarily included in the standard definitions of autism. Third, limited interests and sensory issues could manifest as sociocommunication deficits. As the three factors identified in the present study also had a positive correlation among them, it could be inferred that problems in any of the following three areas such as sociocommunication deficits, sensory issues, and adaptive behavioral deficits should give us a clue that there could be deficits in other areas.
In this study, the factors generated through tetrachoric correlation followed by iterated principal factors could explain maximum variance cumulatively. However, uniqueness provides the percentage of variance for the variable that is unexplained by the common factors. The greater the uniqueness, the more likely that it is more than just measurement error and value for uniqueness >0.6 are considered as high. In this study, all the variables have lower uniqueness, which means that the variable is well explained by the factors.
Any scales developed to identify ASD are ideally tested for its ability to differentiate ASD from intellectual and developmental disability (IDD). The reason is that all levels of IQ can occur in association with autism, but nearly three-quarters of cases would have IDD. Although often associated with some degree of IDD, autism differs from it in terms of severe deficits in social interaction and communication development relatively better or normal nonverbal cognitive abilities. The present study corroborates these findings in terms of sociocommunication deficits explaining 86% variance in autistic symptoms. The new scale also demonstrated a high diagnostic efficiency by yielding a hit rate of 0.89, specificity of 0.90 and a sensitivity of 0.88. These results indicate a diagnostic efficiency on par with BISCUIT. It also has high internal consistency. Therefore, this scale can be used for screening children between 18 months to 84 months. If the children are below 48 months, a cutoff of 5, and if they are between 48 and 84 months, a cut-off of 9 is proposed to indicate autism. Those children identified positively by the test may be followed up for detailed assessment and appropriate intervention. One major limitation is that the groups were not matched for age and developmental quotient. Due to lack of sufficient sample, children with global developmental delay and the children with neurotypical development were categorized as one group to compare with children with autism. Future studies may be planned to overcome this limitation with larger sample size; examine the predictive power of this scale as well as the cost-effectiveness and suitability to use by first-line of professionals. Increasing the sample size highly essential as the sample size was comparatively lesser for the number of items in the scale. More importantly, the scale needs to be validated by the population representing all levels of ASD. A prospective study with a large sample of ASD will further help understand the predictive validity of the study. In conclusion, this scale can be used with young children for identifying autism spectrum disorders.
The authors would like to thank India Vision Foundation for the Reeta Peshawaria Fellowship 2014 to the first author; Professor Johnny L. Matson for giving permission to use BISCUIT; Dr. Guinchat, Vincent for permitting to use the items from his article (Guinchat et al., 2012); All organizations who have participated in this study.
Financial support and sponsorship
Reeta Peshawaria Fellowship 2014.
Conflicts of interest
There are no conflicts of interest.
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Dr. M Thomas Kishore
Department of Clinical Psychology, Dr. Govindaswamy Centre, National Institute of Mental Health and Neurosciences, Hosur Road, Bengaluru - 560 029, Karnataka
Source of Support: None, Conflict of Interest: None
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]